How does Random Forest improve upon a single Decision Tree?

How does Random Forest improve upon a single Decision Tree?

Step 1: Understand that a Decision Tree is a model that makes predictions based on a series of questions about the data.
Step 2: Realize that a single Decision Tree can sometimes make mistakes, especially if it is too complex or too simple.
Step 3: Learn that Random Forest is a method that uses many Decision Trees instead of just one.
Step 4: Know that each tree in the Random Forest makes its own prediction based on the data.
Step 5: Understand that Random Forest combines the predictions from all the trees, usually by averaging them.
Step 6: Recognize that by averaging the predictions, Random Forest reduces the chance of making errors that a single tree might make.
Step 7: Conclude that this process helps improve the overall accuracy of the predictions and reduces overfitting.

Ensemble Learning – Random Forest is an ensemble method that combines multiple decision trees to improve predictive performance.
Overfitting – A single decision tree can overfit the training data, while Random Forest mitigates this by averaging predictions from multiple trees.
Averaging Predictions – Random Forest enhances accuracy by averaging the outputs of various decision trees, leading to more robust predictions.